{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run model module on GCP with labeled threshold tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "PROJECT = \"PROJECT\" # REPLACE WITH YOUR PROJECT ID\n",
    "BUCKET = \"BUCKET\" # REPLACE WITH A BUCKET NAME\n",
    "REGION = \"us-central1\" # REPLACE WITH YOUR REGION e.g. us-central1\n",
    "\n",
    "# Import os environment variables\n",
    "os.environ[\"PROJECT\"] = PROJECT\n",
    "os.environ[\"BUCKET\"] =  BUCKET\n",
    "os.environ[\"REGION\"] = REGION\n",
    "os.environ[\"TFVERSION\"] = \"1.13\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy data over to bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "gcloud storage cp --recursive data/* gs://${BUCKET}/anomaly_detection/data"   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import os environment variables for global sequence shape hyperparameters\n",
    "os.environ[\"SEQ_LEN\"] = str(30)\n",
    "os.environ[\"NUM_FEAT\"] = str(5)\n",
    "\n",
    "# Import os environment variables for global feature hyperparameters\n",
    "os.environ[\"FEAT_NAMES\"] = (\",\").join([\"tag_{}\".format(i) for i in range(int(os.environ[\"NUM_FEAT\"]))])\n",
    "os.environ[\"FEAT_DEFAULTS\"] = (\",\").join([(\";\").join([\"0.0\"] * int(os.environ[\"SEQ_LEN\"]))] * int(os.environ[\"NUM_FEAT\"]))\n",
    "\n",
    "# Import os environment variables for global training hyperparameters\n",
    "os.environ[\"START_DELAY_SECS\"] = str(60)\n",
    "os.environ[\"THROTTLE_SECS\"] = str(120)\n",
    "\n",
    "# Import os environment variables for global threshold hyperparameters\n",
    "os.environ[\"LABELED_TUNE_THRESH\"] = \"True\"\n",
    "os.environ[\"NUM_TIME_ANOM_THRESH\"] = str(300)\n",
    "os.environ[\"NUM_FEAT_ANOM_THRESH\"] = str(300)\n",
    "\n",
    "# Import global dense hyperparameters\n",
    "os.environ[\"ENC_DNN_HIDDEN_UNITS\"] = \"64,32,16\"\n",
    "os.environ[\"LATENT_VECTOR_SIZE\"] = str(8)\n",
    "os.environ[\"DEC_DNN_HIDDEN_UNITS\"] = \"16,32,64\"\n",
    "os.environ[\"TIME_LOSS_WEIGHT\"] = str(1.0)\n",
    "os.environ[\"FEAT_LOSS_WEIGHT\"] = str(1.0)\n",
    "\n",
    "# Import global lstm hyperparameters\n",
    "os.environ[\"REVERSE_LABELS_SEQUENCE\"] = \"True\"\n",
    "os.environ[\"ENC_LSTM_HIDDEN_UNITS\"] = \"64,32,16\"\n",
    "os.environ[\"DEC_LSTM_HIDDEN_UNITS\"] = \"16,32,64\"\n",
    "os.environ[\"LSTM_DROPOUT_OUTPUT_KEEP_PROBS\"] = \"0.9,0.95,1.0\"\n",
    "os.environ[\"DNN_HIDDEN_UNITS\"] = \"1024,256,64\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train reconstruction variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import os environment variables for reconstruction training hyperparameters\n",
    "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/train_norm_seq.csv\".format(BUCKET)\n",
    "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n",
    "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(0)\n",
    "os.environ[\"RECONSTRUCTION_EPOCHS\"] = str(1.0)\n",
    "os.environ[\"TRAIN_EXAMPLES\"] = str(64000)\n",
    "os.environ[\"LEARNING_RATE\"] = str(0.1)\n",
    "os.environ[\"TRAINING_MODE\"] = \"reconstruction\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n",
    "JOBNAME=job_anomaly_detection_reconstruction_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --reconstruction_epochs=${RECONSTRUCTION_EPOCHS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --learning_rate=${LEARNING_RATE} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"dense_autoencoder\" \\\n",
    "  --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n",
    "  --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n",
    "  --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n",
    "  --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n",
    "  --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n",
    "JOBNAME=job_anomaly_detection_reconstruction_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --reconstruction_epochs=${RECONSTRUCTION_EPOCHS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --learning_rate=${LEARNING_RATE} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"lstm_enc_dec_autoencoder\" \\\n",
    "  --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n",
    "  --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n",
    "  --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n",
    "  --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n",
    "  --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n",
    "JOBNAME=job_anomaly_detection_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --reconstruction_epochs=1.0 \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --eval_examples=6400 \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"pca\" \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --autotune_principal_components=\"False\" \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autotune principal components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n",
    "JOBNAME=job_anomaly_detection_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=2000 \\\n",
    "  --reconstruction_epochs=1.0 \\\n",
    "  --train_examples=6400 \\\n",
    "  --eval_examples=6400 \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"pca\" \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --autotune_principal_components=\"True\" \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning of reconstruction hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile hyperparam_reconstruction_dense.yaml\n",
    "trainingInput:\n",
    "  scaleTier: STANDARD_1\n",
    "  hyperparameters:\n",
    "    hyperparameterMetricTag: rmse\n",
    "    goal: MINIMIZE\n",
    "    maxTrials: 30\n",
    "    maxParallelTrials: 1\n",
    "    params:\n",
    "    - parameterName: enc_dnn_hidden_units\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"64 32 16\", \"256 128 16\", \"64 64 64\"]\n",
    "    - parameterName: latent_vector_size\n",
    "      type: INTEGER\n",
    "      minValue: 8\n",
    "      maxValue: 16\n",
    "      scaleType: UNIT_LINEAR_SCALE\n",
    "    - parameterName: dec_dnn_hidden_units\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"16 32 64\", \"16 128 256\", \"64 64 64\"]\n",
    "    - parameterName: train_batch_size\n",
    "      type: INTEGER\n",
    "      minValue: 8\n",
    "      maxValue: 512\n",
    "      scaleType: UNIT_LOG_SCALE\n",
    "    - parameterName: learning_rate\n",
    "      type: DOUBLE\n",
    "      minValue: 0.001\n",
    "      maxValue: 0.1\n",
    "      scaleType: UNIT_LINEAR_SCALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_dense_labeled\n",
    "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --config=hyperparam_reconstruction_dense.yaml \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n",
    "  --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --seq_len=30 \\\n",
    "  --num_feat=5 \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=0 \\\n",
    "  --reconstruction_epochs=1.0 \\\n",
    "  --train_examples=64000 \\\n",
    "  --start_delay_secs=60 \\\n",
    "  --throttle_secs=120 \\\n",
    "  --training_mode=\"reconstruction\" \\\n",
    "  --labeled_tune_thresh=True \\\n",
    "  --num_time_anom_thresh=300 \\\n",
    "  --num_feat_anom_thresh=300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile hyperparam_reconstruction_lstm.yaml\n",
    "trainingInput:\n",
    "  scaleTier: STANDARD_1\n",
    "  hyperparameters:\n",
    "    hyperparameterMetricTag: rmse\n",
    "    goal: MINIMIZE\n",
    "    maxTrials: 30\n",
    "    maxParallelTrials: 1\n",
    "    params:\n",
    "    - parameterName: enc_lstm_hidden_units\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"64 32 16\", \"256 128 16\", \"64 64 64\"]\n",
    "    - parameterName: dec_lstm_hidden_units\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"16 32 64\", \"16 128 256\", \"64 64 64\"]\n",
    "    - parameterName: lstm_dropout_output_keep_probs\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"0.9 1.0 1.0\", \"0.95 0.95 1.0\", \"0.95 0.95 0.95\"]\n",
    "    - parameterName: dnn_hidden_units\n",
    "      type: CATEGORICAL\n",
    "      categoricalValues: [\"256 128 64\", \"256 128 16\", \"64 64 64\"]\n",
    "    - parameterName: train_batch_size\n",
    "      type: INTEGER\n",
    "      minValue: 8\n",
    "      maxValue: 512\n",
    "      scaleType: UNIT_LOG_SCALE\n",
    "    - parameterName: learning_rate\n",
    "      type: DOUBLE\n",
    "      minValue: 0.001\n",
    "      maxValue: 0.1\n",
    "      scaleType: UNIT_LINEAR_SCALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_lstm_labeled\n",
    "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --config=hyperparam_reconstruction_lstm.yaml \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n",
    "  --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --seq_len=30 \\\n",
    "  --num_feat=5 \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=0 \\\n",
    "  --reconstruction_epochs=1.0 \\\n",
    "  --train_examples=64000 \\\n",
    "  --start_delay_secs=60 \\\n",
    "  --throttle_secs=120 \\\n",
    "  --training_mode=\"reconstruction\" \\\n",
    "  --labeled_tune_thresh=True \\\n",
    "  --num_time_anom_thresh=300 \\\n",
    "  --num_feat_anom_thresh=300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile hyperparam_reconstruction_pca.yaml\n",
    "trainingInput:\n",
    "  scaleTier: STANDARD_1\n",
    "  hyperparameters:\n",
    "    hyperparameterMetricTag: rmse\n",
    "    goal: MINIMIZE\n",
    "    maxTrials: 30\n",
    "    maxParallelTrials: 1\n",
    "    params:\n",
    "    - parameterName: k_principal_components_time\n",
    "      type: INTEGER\n",
    "      minValue: 2\n",
    "      maxValue: 10\n",
    "      scaleType: UNIT_LINEAR_SCALE\n",
    "    - parameterName: k_principal_components_feat\n",
    "      type: INTEGER\n",
    "      minValue: 2\n",
    "      maxValue: 10\n",
    "      scaleType: UNIT_LINEAR_SCALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_pca_labeled\n",
    "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud storage rm --recursive --continue-on-error ${OUTDIR}\n",    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --config=hyperparam_reconstruction_pca.yaml \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n",
    "  --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --seq_len=30 \\\n",
    "  --num_feat=5 \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=0 \\\n",
    "  --reconstruction_epochs=1.0 \\\n",
    "  --train_examples=64000 \\\n",
    "  --eval_examples=6400 \\\n",
    "  --start_delay_secs=60 \\\n",
    "  --throttle_secs=120 \\\n",
    "  --training_mode=\"reconstruction\" \\\n",
    "  --labeled_tune_thresh=True \\\n",
    "  --num_time_anom_thresh=300 \\\n",
    "  --num_feat_anom_thresh=300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train error distribution variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import os environment variables for error dist training hyperparameters\n",
    "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n",
    "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n",
    "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(2000)\n",
    "os.environ[\"TRAIN_EXAMPLES\"] = str(6400)\n",
    "os.environ[\"TRAINING_MODE\"] = \"calculate_error_distribution_statistics\"\n",
    "os.environ[\"EPS\"] = \"1e-12\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n",
    "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"dense_autoencoder\" \\\n",
    "  --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n",
    "  --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n",
    "  --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n",
    "  --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n",
    "  --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --eps=${EPS} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n",
    "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"lstm_enc_dec_autoencoder\" \\\n",
    "  --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n",
    "  --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n",
    "  --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n",
    "  --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n",
    "  --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --eps=${EPS} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n",
    "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=2200 \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"pca\" \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --eps=${EPS} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tune anomaly thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import os environment variables for tune threshold training hyperparameters\n",
    "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(2200)\n",
    "os.environ[\"TRAIN_EXAMPLES\"] = str(12800)\n",
    "os.environ[\"TRAINING_MODE\"] = \"tune_anomaly_thresholds\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import os environment variables for labeled tune threshold training hyperparameters\n",
    "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/labeled_val_mixed_seq.csv\".format(BUCKET)\n",
    "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/labeled_val_mixed_seq.csv\".format(BUCKET)\n",
    "os.environ[\"MIN_TIME_ANOM_THRESH\"] = str(1.0)\n",
    "os.environ[\"MAX_TIME_ANOM_THRESH\"] = str(20.0)\n",
    "os.environ[\"MIN_FEAT_ANOM_THRESH\"] = str(20.0)\n",
    "os.environ[\"MAX_FEAT_ANOM_THRESH\"] = str(80.0)\n",
    "os.environ[\"F_SCORE_BETA\"] = str(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n",
    "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"dense_autoencoder\" \\\n",
    "  --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n",
    "  --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n",
    "  --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n",
    "  --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n",
    "  --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n",
    "  --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n",
    "  --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n",
    "  --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n",
    "  --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n",
    "  --f_score_beta=${F_SCORE_BETA}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n",
    "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"lstm_enc_dec_autoencoder\" \\\n",
    "  --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n",
    "  --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n",
    "  --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n",
    "  --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n",
    "  --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n",
    "  --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n",
    "  --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n",
    "  --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n",
    "  --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n",
    "  --f_score_beta=${F_SCORE_BETA}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n",
    "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n",
    "echo ${OUTDIR} ${REGION} ${JOBNAME}\n",
    "gcloud ml-engine jobs submit training ${JOBNAME} \\\n",
    "  --region=${REGION} \\\n",
    "  --module-name=trainer.task \\\n",
    "  --package-path=$PWD/anomaly_detection_module/trainer \\\n",
    "  --job-dir=${OUTDIR} \\\n",
    "  --staging-bucket=gs://${BUCKET} \\\n",
    "  --scale-tier=STANDARD_1 \\\n",
    "  --runtime-version=${TFVERSION} \\\n",
    "  -- \\\n",
    "  --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n",
    "  --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n",
    "  --output_dir=${OUTDIR} \\\n",
    "  --job-dir=./tmp \\\n",
    "  --seq_len=${SEQ_LEN} \\\n",
    "  --num_feat=${NUM_FEAT} \\\n",
    "  --feat_names=${FEAT_NAMES} \\\n",
    "  --feat_defaults=${FEAT_DEFAULTS} \\\n",
    "  --train_batch_size=32 \\\n",
    "  --eval_batch_size=32 \\\n",
    "  --previous_train_steps=2400 \\\n",
    "  --train_examples=${TRAIN_EXAMPLES} \\\n",
    "  --start_delay_secs=${START_DELAY_SECS} \\\n",
    "  --throttle_secs=${THROTTLE_SECS} \\\n",
    "  --model_type=\"pca\" \\\n",
    "  --training_mode=${TRAINING_MODE} \\\n",
    "  --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n",
    "  --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n",
    "  --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n",
    "  --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n",
    "  --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n",
    "  --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n",
    "  --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n",
    "  --f_score_beta=${F_SCORE_BETA}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "MODEL_NAME=\"anomaly_detection_dense_labeled\"\n",
    "MODEL_VERSION=\"v1\"\n",
    "MODEL_LOCATION=$(gcloud storage ls gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled/export/exporter/ | tail -1)\n",    "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n",
    "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n",
    "#gcloud ml-engine models delete ${MODEL_NAME}\n",
    "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n",
    "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "MODEL_NAME=\"anomaly_detection_lstm_labeled\"\n",
    "MODEL_VERSION=\"v1\"\n",
    "MODEL_LOCATION=$(gcloud storage ls gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled/export/exporter/ | tail -1)\n",    "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n",
    "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n",
    "#gcloud ml-engine models delete ${MODEL_NAME}\n",
    "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n",
    "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "MODEL_NAME=\"anomaly_detection_pca_labeled\"\n",
    "MODEL_VERSION=\"v1\"\n",
    "MODEL_LOCATION=$(gcloud storage ls gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled/export/exporter/ | tail -1)\n",    "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n",
    "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n",
    "#gcloud ml-engine models delete ${MODEL_NAME}\n",
    "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n",
    "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "UNLABELED_CSV_COLUMNS = [\"tag_{0}\".format(tag) for tag in range(0, 5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labeled_test_mixed_sequences_array.shape = (12800, 6)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "labeled_test_mixed_sequences_array = np.loadtxt(\n",
    "    fname=\"data/labeled_test_mixed_seq.csv\", dtype=str, delimiter=\",\")\n",
    "print(\"labeled_test_mixed_sequences_array.shape = {}\".format(\n",
    "    labeled_test_mixed_sequences_array.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels = ['0' '0' '0' '0' '0' '0' '1' '1' '1' '0']\n"
     ]
    }
   ],
   "source": [
    "number_of_prediction_instances = 10\n",
    "print(\"labels = {}\".format(\n",
    "  labeled_test_mixed_sequences_array[0:number_of_prediction_instances, -1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GCloud ML-Engine prediction from deployed model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_normal_string_list = labeled_test_mixed_sequences_array.tolist()[0:number_of_prediction_instances]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Format dataframe to instances list to get sent to ML-Engine\n",
    "instances = [{UNLABELED_CSV_COLUMNS[i]: example[i]\n",
    "              for i in range(len(UNLABELED_CSV_COLUMNS))} \n",
    "             for example in labeled_test_mixed_sequences_array.tolist()[0:number_of_prediction_instances]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " {'tag_0': '0.72579761;2.47522531;1.327882;-0.75268684;-1.1781596;1.8190688;2.75720961;0.36062891;-0.83751352;-0.71114497;1.94022901;1.66131344;-0.31676063;-0.97258012;0.64972194;2.63356231;1.00750943;-1.47431186;-0.72892581;1.50185207;2.21181981;0.2661509;-1.25935447;0.30660896;1.88079632;1.61985348;-0.05001294;-0.84116216;0.34933468;2.13577794',\n",
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       "  'tag_2': '0.90848602;2.21676242;1.85921972;-0.24197257;-0.9100976;0.00275013;1.00053774;1.40055928;0.74702449;-0.50267597;-0.84651481;0.2292874;1.98407809;2.26690591;0.9022541;-1.13558066;-0.21949626;1.04933645;1.6770574;1.55207594;-0.14044969;-0.86823706;-0.28564309;1.74831191;1.70918509;1.12008136;-0.04343257;-0.9626134;-0.09400303;2.07010619',\n",
       "  'tag_3': '0.95364124;1.73729486;-0.46975947;-0.60177623;1.60340907;1.05856077;-1.1101516;0.45645482;2.09716668;-0.21975787;-0.38319073;2.05270447;1.16664725;-1.21537023;0.25826675;2.25034501;-0.60087805;-0.49597547;1.33748965;1.42932051;-0.60206741;1.00846474;1.58231814;-0.5112769;-0.98310747;1.87652975;0.88289004;-1.03286975;0.93315793;2.15740461',\n",
       "  'tag_4': '0.40835687;1.56538177;1.37825225;0.08545293;-0.70862794;0.88269748;1.16281691;1.07729888;-0.90948711;-0.60861432;1.48940031;2.04036648;0.14156203;-0.44991055;-0.2542472;1.37231589;1.79903228;0.09845316;-0.64955625;0.32648146;1.18872066;1.24506551;-0.0067401;-0.50290517;0.33286966;1.43410147;1.71245797;-0.58743568;-0.95471011;0.93948165'},\n",
       " {'tag_0': '0.32586937;2.08483849;1.75075176;-1.3336731;-1.17097551;0.97406564;1.7887734;0.08011179;-1.08188674;-0.05479934;1.99891319;1.61133871;-0.71490799;-0.99111498;0.87641865;2.61951522;0.81368508;-0.72022372;-1.31567318;0.98415183;1.75673683;0.24764211;-1.37138841;-0.00938338;2.49622452;1.93360966;-0.8683438;-1.02346433;0.22796142;2.17651303',\n",
       "  'tag_1': '0.88830138;1.40762486;-0.89037037;-0.24249388;1.91719852;-0.01578304;-0.08442717;1.55637788;-0.12647233;-0.38200814;1.6873275;0.98669163;-1.14192299;1.31623504;0.53457028;-0.55379481;1.41801087;1.68637843;-0.93578977;0.94078648;1.1668302;-0.32543639;0.656733;1.68649731;0.12277812;0.29430548;1.32494733;0.39247261;-0.66217234;1.33253825',\n",
       "  'tag_2': '0.81516693;1.60512581;1.6907453;0.22479972;-0.95582798;-0.27743592;0.69123689;2.17858567;1.0722736;-0.30677606;-0.75284401;-0.34243141;1.70903897;1.86075445;1.07625646;-0.75971642;-1.11335946;0.37604343;1.86715537;1.09562839;-0.33711711;-1.36609983;-0.22295621;0.90491489;1.75689099;0.92176145;-0.77558837;-1.15932892;0.60134914;1.8090888',\n",
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       " {'tag_0': '0.04002373;2.77047149;1.18899767;-0.60047226;-1.33342528;0.9332609;1.79713745;0.12207074;-1.54002434;0.12563447;1.65582437;2.16662805;-0.27858593;-1.33363261;0.63004838;2.60991971;1.54024527;-0.56917252;-0.72655791;1.15147733;2.50307969;0.34091677;-1.78544364;0.0469913;2.3052051;1.82387952;-0.12610546;-1.5179853;1.08815115;2.71393575',\n",
       "  'tag_1': '0.77316403;1.78993695;-0.00996767;0.06333012;1.74399522;-0.40204323;-0.3841756;2.08135886;-0.05024028;-0.53364949;2.07842082;0.61253916;-0.539925;0.96458172;0.93248708;-0.46253491;1.1020334;1.59779402;-0.86248362;0.97942725;1.28409853;-0.41827883;-0.21428836;1.3041642;0.13853662;-0.37642735;1.32348276;-0.03971839;-0.28628336;1.53879333',\n",
       "  'tag_2': '0.5064642;1.73569998;1.15883707;0.32626716;-0.93921306;-0.61178983;1.45841529;1.66214296;0.94871562;-0.40083787;-1.33746892;-0.17665792;1.81168659;1.70129509;1.07749217;-0.68119423;-0.23273985;0.39928992;1.4249041;1.33871969;0.37569222;-0.40994081;0.21717019;1.18499547;2.23490559;0.54602336;-0.17562612;-0.69806029;0.52470708;2.11374314',\n",
       "  'tag_3': '0.00495428;1.73058158;0.32864859;-0.12331526;1.38895531;0.80194698;-0.70576781;0.93858581;2.34687482;0.0124869;-0.42339452;2.1535986;0.88734299;-0.80289269;0.40426268;2.2888359;-0.22907384;-0.26897599;1.54588075;1.350909;-0.59548962;0.19248556;2.09446286;-0.0599024;-0.86764715;1.94053306;1.24092046;-1.21382899;0.28861945;1.45708077',\n",
       "  'tag_4': '0.09601987;1.79091122;1.51241607;0.15140355;-0.22029392;0.35311483;2.01294182;0.74675243;-0.91219142;-0.11479409;1.471586;2.10590009;0.79785938;-0.51961445;-0.47316746;0.80397399;1.70220473;0.79817527;-0.8511636;-0.14538853;1.10266323;1.11418798;-0.05283848;-0.14234928;0.33590364;1.48447235;1.6688788;-0.64356563;-0.74619109;0.94730833'}]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dense Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "response = {'predictions': [{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.66491856, 0.14611996, 0.93582327, 0.55496827, 0.09135211], [1.9145195, 1.91715205, 1.42382616, 1.63017531, 2.01834776], [1.75234653, 0.37882894, 2.07416534, 0.17487062, 0.97056041], [0.78400576, 0.21415584, 0.66961254, 0.14967874, 0.1594919], [0.75609776, 2.12791802, 0.45530108, 2.12154598, 0.62361487], [0.84197907, 0.0619942, 0.33779627, 0.98646297, 0.47084415], [2.33069302, 0.11572278, 1.29564858, 0.89614069, 1.44724272], [0.67758695, 1.93012199, 1.57718875, 0.16462197, 1.15545605], [1.60418555, 0.6541758, 1.62194094, 1.48088977, 0.64437793], [0.16573821, 0.68204207, 0.50371325, 0.2009814, 0.18398029], [1.65752209, 1.56716207, 0.68795334, 0.7492661, 1.1087019], [1.38322258, 0.75419849, 0.21823157, 1.41074009, 2.06215052], [0.65379731, 0.37778253, 1.67758936, 1.49459742, 0.45822295], [0.98907577, 1.57716806, 1.87901361, 0.78390433, 0.61682919], [0.85974301, 1.37025903, 0.89202323, 0.96362317, 0.29801659], [2.28947191, 1.14077495, 1.15923235, 2.35191643, 0.82097915], [1.30827902, 1.15195495, 0.39328089, 0.01970021, 1.61374037], [1.28121539, 1.68996712, 1.03425895, 0.30345158, 0.41494366], [0.84517686, 0.81952461, 1.62499189, 1.84212884, 1.0210058], [1.16128253, 0.85177925, 1.47040884, 0.63518639, 0.21951907], [2.54710653, 1.21750258, 0.40736898, 1.26010881, 1.65998406], [0.07715313, 0.15392611, 1.1339056, 0.84082203, 1.83563618], [0.99222116, 0.18813038, 0.29514493, 2.33136081, 0.25813724], [0.14657169, 2.1498899, 1.74666228, 0.67124369, 0.13240912], [2.05595855, 0.22421678, 2.22234859, 0.70904769, 0.38837674], [1.51383899, 0.30835851, 0.66564559, 1.48210016, 1.99312546], [0.53623582, 1.58292183, 0.7699364, 0.858758, 1.22683365], [0.75514892, 0.08034281, 0.64337751, 1.31116881, 0.4224495], [0.48173323, 0.51606627, 0.32518635, 0.49555797, 0.18025518], [2.3100255, 2.0111497, 1.48844008, 1.40730677, 0.08359485]], 'mahalanobis_dist_time': [1.793566846620438, 2.7994798526888545, 2.8774830168347485, 2.008680772788462, 3.3879531623478227, 1.663625586678576, 2.3949866432177114, 2.4593933497764597, 1.4139920591441237, 1.9227839936915445, 1.6168853466082886, 2.7006448866367565, 2.1289310745947327, 1.8126899773492855, 1.3722570330367163, 2.362495403099553, 2.469458921190661, 2.0756610236199378, 2.2136885875330248, 1.4865407953940468, 2.4129094293683804, 3.507706757868135, 3.014598981355669, 2.904520888345697, 3.00484213563406, 2.5925885898919145, 1.8541457837809983, 1.7159654648030584, 1.7643617466760166, 3.215679588381463], 'mahalanobis_dist_feat': [5.297820034999684, 5.010979246751353, 5.936222704742021, 6.451951586925733, 5.869318526257837], 'feat_anom_flags': 0, 'X_feat_abs_recon_err': [[0.66491856, 0.14611996, 0.93582327, 0.55496827, 0.09135211], [1.9145195, 1.91715205, 1.42382616, 1.63017531, 2.01834776], [1.75234653, 0.37882894, 2.07416534, 0.17487062, 0.97056041], [0.78400576, 0.21415584, 0.66961254, 0.14967874, 0.1594919], [0.75609776, 2.12791802, 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1.51241607], [0.60047226, 0.06333012, 0.32626716, 0.12331526, 0.15140355], [1.33342528, 1.74399522, 0.93921306, 1.38895531, 0.22029392], [0.9332609, 0.40204323, 0.61178983, 0.80194698, 0.35311483], [1.79713745, 0.3841756, 1.45841529, 0.70576781, 2.01294182], [0.12207074, 2.08135886, 1.66214296, 0.93858581, 0.74675243], [1.54002434, 0.05024028, 0.94871562, 2.34687482, 0.91219142], [0.12563447, 0.53364949, 0.40083787, 0.0124869, 0.11479409], [1.65582437, 2.07842082, 1.33746892, 0.42339452, 1.471586], [2.16662805, 0.61253916, 0.17665792, 2.1535986, 2.10590009], [0.27858593, 0.539925, 1.81168659, 0.88734299, 0.79785938], [1.33363261, 0.96458172, 1.70129509, 0.80289269, 0.51961445], [0.63004838, 0.93248708, 1.07749217, 0.40426268, 0.47316746], [2.60991971, 0.46253491, 0.68119423, 2.2888359, 0.80397399], [1.54024527, 1.1020334, 0.23273985, 0.22907384, 1.70220473], [0.56917252, 1.59779402, 0.39928992, 0.26897599, 0.79817527], [0.72655791, 0.86248362, 1.4249041, 1.54588075, 0.8511636], [1.15147733, 0.97942725, 1.33871969, 1.350909, 0.14538853], [2.50307969, 1.28409853, 0.37569222, 0.59548962, 1.10266323], [0.34091677, 0.41827883, 0.40994081, 0.19248556, 1.11418798], [1.78544364, 0.21428836, 0.21717019, 2.09446286, 0.05283848], [0.0469913, 1.3041642, 1.18499547, 0.0599024, 0.14234928], [2.3052051, 0.13853662, 2.23490559, 0.86764715, 0.33590364], [1.82387952, 0.37642735, 0.54602336, 1.94053306, 1.48447235], [0.12610546, 1.32348276, 0.17562612, 1.24092046, 1.6688788], [1.5179853, 0.03971839, 0.69806029, 1.21382899, 0.64356563], [1.08815115, 0.28628336, 0.52470708, 0.28861945, 0.74619109], [2.71393575, 1.53879333, 2.11374314, 1.45708077, 0.94730833]]}]}\n"
     ]
    }
   ],
   "source": [
    "# Send instance dictionary to receive response from ML-Engine for online prediction\n",
    "from googleapiclient import discovery\n",
    "from oauth2client.client import GoogleCredentials\n",
    "import json\n",
    "\n",
    "credentials = GoogleCredentials.get_application_default()\n",
    "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n",
    "\n",
    "request_data = {\"instances\": instances}\n",
    "\n",
    "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_dense_labeled\", \"v1\")\n",
    "response = api.projects().predict(body = request_data, name = parent).execute()\n",
    "print(\"response = {}\".format(response))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LSTM Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "response = {'predictions': [{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.41491645314022535, 0.17814782047161393, 0.6791140559628012, 0.1871596458471131, 0.3532771634912568], [1.6645173931402253, 1.5928842695283862, 1.167116945962801, 1.262366685847113, 1.5737184865087435], [1.5023444231402254, 0.7030967204716139, 1.817456125962801, 0.5426792441528869, 0.5259311365087432], [1.0340078668597747, 0.11011194047161393, 0.4129033259628012, 0.517487364152887, 0.6041211734912568], [1.0060998668597747, 1.8036502395283862, 0.7120102940371988, 1.7537373558471132, 1.0682441434912568], [0.5919769631402254, 0.3862619804716139, 0.5945054840371988, 0.6186543458471131, 0.026214876508743212], [2.0806909131402254, 0.20854500047161392, 1.038939365962801, 1.263949314152887, 1.002613446508743], [0.4275848431402253, 1.605854209528386, 1.320479535962801, 0.20318665415288695, 0.7108267765087432], [1.8541876568597746, 0.32990801952838605, 1.365231725962801, 1.1130811458471133, 1.0890072034912568], 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{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.07281104685977469, 0.38603374952838604, 0.16303782403719883, 0.06515406584711303, 0.14251572650874322], [2.1147090531402255, 1.5426797195283861, 1.080757525962801, 1.862286665847113, 0.9089083965087431], [1.0866816431402253, 0.41598273047161394, 1.643732485962801, 0.8539331741528869, 0.5129931265087432], [0.6900083068597747, 0.5040877604716139, 0.4761440840371988, 0.730085704152887, 0.3171473534912568], [1.5096358868597746, 1.901314049528386, 1.5682721040371987, 1.088847645847113, 1.007563133491257], [1.4803149531402253, 0.4708057104716139, 0.15721215403719882, 0.39150852584711304, 0.5047497165087432], [2.3352116431402252, 0.7310927704716139, 0.9016416459628013, 1.5535176641528872, 1.087574486508743], [0.1364564368597747, 1.233921349528386, 1.2793462959628012, 0.07903569584711306, 0.7386666965087433], [1.3631069668597746, 0.015966589528386088, 1.0480828259628012, 1.4262653758471129, 0.4739908634912568], [0.5719913668597747, 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1.5549773759628014, 0.5195343658471131, 0.3532301065087432], [1.5836347168597746, 0.6403139395283861, 1.444585875962801, 1.170701314152887, 0.9642437234912568], [0.38004627314022527, 0.6082192995283862, 0.8207829559628012, 0.03645405584711303, 0.9177967334912568], [2.359917603140225, 0.7868026904716139, 0.9379034440371988, 1.9210272758471132, 0.3593447165087432], [1.2902431631402254, 0.7777656195283862, 0.4894490640371988, 0.5968824641528869, 1.2575754565087434], [0.8191746268597746, 1.2735262395283862, 0.1425807059628012, 0.636784614152887, 0.3535459965087432], [0.9765600168597748, 1.186751400471614, 1.168194885962801, 1.178072125847113, 1.2957928734912567], [0.9014752231402254, 0.655159469528386, 1.082010475962801, 0.983100375847113, 0.5900178034912568], [2.253077583140225, 0.9598307495283862, 0.1189830059628012, 0.963298244152887, 0.6580339565087433], [0.09091466314022528, 0.7425466104716139, 0.6666500240371989, 0.17532306415288695, 0.6695587065087433], [2.0354457468597746, 0.5385561404716139, 0.039539024037198806, 1.7266542358471133, 0.4974677534912568], [0.20301080685977468, 0.9798964195283861, 0.9282862559628012, 0.427711024152887, 0.5869785534912568], [2.055202993140225, 0.18573116047161392, 1.978196375962801, 1.235455774152887, 0.1087256334912568], [1.5738774131402253, 0.7006951304716139, 0.28931414596280114, 1.572724435847113, 1.0398430765087432], [0.3761075668597747, 0.9992149795283862, 0.4323353340371988, 0.8731118358471129, 1.2242495265087432], [1.7679874068597747, 0.3639861704716139, 0.9547695040371988, 1.581637614152887, 1.0881949034912568], [0.8381490431402254, 0.610551140471614, 0.2679978659628012, 0.07918917415288695, 1.1908203634912566], [2.4639336431402254, 1.2145255495283862, 1.857033925962801, 1.0892721458471128, 0.5026790565087432]]}]}\n"
     ]
    }
   ],
   "source": [
    "# Send instance dictionary to receive response from ML-Engine for online prediction\n",
    "from googleapiclient import discovery\n",
    "from oauth2client.client import GoogleCredentials\n",
    "import json\n",
    "\n",
    "credentials = GoogleCredentials.get_application_default()\n",
    "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n",
    "\n",
    "request_data = {\"instances\": instances}\n",
    "\n",
    "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_lstm_labeled\", \"v1\")\n",
    "response = api.projects().predict(body = request_data, name = parent).execute()\n",
    "print(\"response = {}\".format(response))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "response = {'predictions': [{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.1728792878886382, 0.38050960188316113, 0.4029056164752808, 0.014843864134493515, 0.37395442642699095], [0.4715622077460193, 1.347448091416575, 0.785070509241718, 0.8864118912527625, 0.5991388918543972], [0.07542798900316616, 0.9324050785330078, 1.4750373173605118, 0.842387971324521, 0.09149046337009026], [0.03565646553488011, 0.28264294850716104, 0.20999200847594207, 0.548774838816692, 0.03581776391927072], [0.04857788467463031, 1.6307784060092017, 0.9157590530057195, 1.7208385907098551, 0.43585294321592793], [0.08302349375710694, 0.594693984257074, 0.8856290524187558, 0.4176409110762759, 0.12889025356158673], [0.09530165447389516, 0.45055417216711735, 0.665313418520593, 1.623702553827909, 0.10392648557085626], [0.2739479466248842, 1.3930425583357011, 1.0185947463993117, 0.4249053603401392, 0.45873233970642757], [0.13365573982912693, 0.17217907084086995, 1.1986905131399577, 1.1517720570728396, 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     ]
    }
   ],
   "source": [
    "# Send instance dictionary to receive response from ML-Engine for online prediction\n",
    "from googleapiclient import discovery\n",
    "from oauth2client.client import GoogleCredentials\n",
    "import json\n",
    "\n",
    "credentials = GoogleCredentials.get_application_default()\n",
    "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n",
    "\n",
    "request_data = {\"instances\": instances}\n",
    "\n",
    "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_pca_labeled\", \"v1\")\n",
    "response = api.projects().predict(body = request_data, name = parent).execute()\n",
    "print(\"response = {}\".format(response))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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